A Two-stage AI/ML Framework Integrating Ayurvedic Phenotyping and Clinical Data for Early Alzheimer’s Risk Detection. This work presents a neuro-integrative AI/ML framework combining Ayurvedic phenotyping, clinical biomarkers, and digital health signals for Stage-0 dementia risk screening. The system uses a two-stage pipeline: Stage 1 performs Prakriti classification, while Stage 2 combines clinical and lifestyle features for personalized risk prediction. Validation using synthetic datasets demonstrates strong feasibility for scalable, low-cost early dementia screening in resource-limited settings.